Overview

Dataset statistics

Number of variables13
Number of observations6819
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory692.7 KiB
Average record size in memory104.0 B

Variable types

Numeric12
Categorical1

Alerts

Debt ratio % is highly correlated with Current Liability to Assets and 5 other fieldsHigh correlation
Current Liability to Assets is highly correlated with Debt ratio % and 5 other fieldsHigh correlation
Borrowing dependency is highly correlated with Debt ratio % and 5 other fieldsHigh correlation
Current Liability to Current Assets is highly correlated with Debt ratio % and 5 other fieldsHigh correlation
Liability to Equity is highly correlated with Debt ratio % and 5 other fieldsHigh correlation
Current Liabilities/Equity is highly correlated with Debt ratio % and 5 other fieldsHigh correlation
Net Income to Total Assets is highly correlated with ROA(A) before interest and % after tax and 3 other fieldsHigh correlation
ROA(A) before interest and % after tax is highly correlated with Net Income to Total Assets and 3 other fieldsHigh correlation
ROA(B) before interest and depreciation after tax is highly correlated with Net Income to Total Assets and 3 other fieldsHigh correlation
ROA(C) before interest and depreciation before interest is highly correlated with Net Income to Total Assets and 3 other fieldsHigh correlation
Net worth/Assets is highly correlated with Debt ratio % and 5 other fieldsHigh correlation
Persistent EPS in the Last Four Seasons is highly correlated with Net Income to Total Assets and 3 other fieldsHigh correlation
Debt ratio % is highly correlated with Current Liability to Assets and 1 other fieldsHigh correlation
Current Liability to Assets is highly correlated with Debt ratio % and 1 other fieldsHigh correlation
Borrowing dependency is highly correlated with Liability to Equity and 1 other fieldsHigh correlation
Liability to Equity is highly correlated with Borrowing dependency and 1 other fieldsHigh correlation
Current Liabilities/Equity is highly correlated with Borrowing dependency and 1 other fieldsHigh correlation
Net Income to Total Assets is highly correlated with ROA(A) before interest and % after tax and 3 other fieldsHigh correlation
ROA(A) before interest and % after tax is highly correlated with Net Income to Total Assets and 3 other fieldsHigh correlation
ROA(B) before interest and depreciation after tax is highly correlated with Net Income to Total Assets and 3 other fieldsHigh correlation
ROA(C) before interest and depreciation before interest is highly correlated with Net Income to Total Assets and 3 other fieldsHigh correlation
Net worth/Assets is highly correlated with Debt ratio % and 1 other fieldsHigh correlation
Persistent EPS in the Last Four Seasons is highly correlated with Net Income to Total Assets and 3 other fieldsHigh correlation
Debt ratio % is highly correlated with Current Liability to Assets and 5 other fieldsHigh correlation
Current Liability to Assets is highly correlated with Debt ratio % and 3 other fieldsHigh correlation
Borrowing dependency is highly correlated with Debt ratio % and 3 other fieldsHigh correlation
Current Liability to Current Assets is highly correlated with Debt ratio % and 3 other fieldsHigh correlation
Liability to Equity is highly correlated with Debt ratio % and 5 other fieldsHigh correlation
Current Liabilities/Equity is highly correlated with Debt ratio % and 5 other fieldsHigh correlation
Net Income to Total Assets is highly correlated with ROA(A) before interest and % after tax and 3 other fieldsHigh correlation
ROA(A) before interest and % after tax is highly correlated with Net Income to Total Assets and 3 other fieldsHigh correlation
ROA(B) before interest and depreciation after tax is highly correlated with Net Income to Total Assets and 3 other fieldsHigh correlation
ROA(C) before interest and depreciation before interest is highly correlated with Net Income to Total Assets and 3 other fieldsHigh correlation
Net worth/Assets is highly correlated with Debt ratio % and 5 other fieldsHigh correlation
Persistent EPS in the Last Four Seasons is highly correlated with Net Income to Total Assets and 3 other fieldsHigh correlation
Debt ratio % is highly correlated with Current Liability to Assets and 5 other fieldsHigh correlation
Current Liability to Assets is highly correlated with Debt ratio % and 4 other fieldsHigh correlation
Borrowing dependency is highly correlated with Debt ratio % and 6 other fieldsHigh correlation
Current Liability to Current Assets is highly correlated with Borrowing dependency and 1 other fieldsHigh correlation
Liability to Equity is highly correlated with Debt ratio % and 4 other fieldsHigh correlation
Current Liabilities/Equity is highly correlated with Debt ratio % and 6 other fieldsHigh correlation
Net Income to Total Assets is highly correlated with ROA(A) before interest and % after tax and 3 other fieldsHigh correlation
ROA(A) before interest and % after tax is highly correlated with Borrowing dependency and 4 other fieldsHigh correlation
ROA(B) before interest and depreciation after tax is highly correlated with Net Income to Total Assets and 3 other fieldsHigh correlation
ROA(C) before interest and depreciation before interest is highly correlated with Net Income to Total Assets and 3 other fieldsHigh correlation
Net worth/Assets is highly correlated with Debt ratio % and 5 other fieldsHigh correlation
Persistent EPS in the Last Four Seasons is highly correlated with Debt ratio % and 6 other fieldsHigh correlation
Borrowing dependency is highly skewed (γ1 = 20.83889089) Skewed
Liability to Equity is highly skewed (γ1 = 27.45946714) Skewed
Current Liabilities/Equity is highly skewed (γ1 = 23.79916473) Skewed
Current Liability to Assets has unique values Unique
Current Liability to Current Assets has unique values Unique
Liability to Equity has unique values Unique
Current Liabilities/Equity has unique values Unique
Net Income to Total Assets has unique values Unique

Reproduction

Analysis started2021-12-26 21:12:19.953948
Analysis finished2021-12-26 21:12:55.427563
Duration35.47 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Debt ratio %
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4208
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.113177085
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size53.4 KiB
2021-12-26T18:12:55.611498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03221876228
Q10.07289052816
median0.1114067177
Q30.1488043051
95-th percentile0.2026181334
Maximum1
Range1
Interquartile range (IQR)0.07591377694

Descriptive statistics

Standard deviation0.05392030606
Coefficient of variation (CV)0.4764242344
Kurtosis10.72972726
Mean0.113177085
Median Absolute Deviation (MAD)0.03803246969
Skewness0.9807961168
Sum771.7545424
Variance0.002907399406
MonotonicityNot monotonic
2021-12-26T18:12:55.828687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11291834216
 
0.1%
0.089155606616
 
0.1%
0.12821598096
 
0.1%
0.11947879196
 
0.1%
0.12340901536
 
0.1%
0.1154578716
 
0.1%
0.14015781366
 
0.1%
0.1067206826
 
0.1%
0.14844151535
 
0.1%
0.075762614515
 
0.1%
Other values (4198)6761
99.1%
ValueCountFrequency (%)
01
< 0.1%
0.00027209239051
< 0.1%
0.0003627898541
< 0.1%
0.00057441726881
< 0.1%
0.0010883695621
< 0.1%
0.0015116243921
< 0.1%
0.0018744142461
< 0.1%
0.002086041661
< 0.1%
0.0031441787341
< 0.1%
0.0035372010761
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.5254104061
< 0.1%
0.33848293381
< 0.1%
0.33189225141
< 0.1%
0.32693412341
< 0.1%
0.32137134561
< 0.1%
0.31275508661
< 0.1%
0.30537835961
< 0.1%
0.29519001121
< 0.1%
0.29370861931
< 0.1%

Current Liability to Assets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct6819
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09067279457
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size53.4 KiB
2021-12-26T18:12:56.066862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02545154402
Q10.05330127643
median0.08270479498
Q30.1195229935
95-th percentile0.1864716616
Maximum1
Range1
Interquartile range (IQR)0.06622171704

Descriptive statistics

Standard deviation0.05028985667
Coefficient of variation (CV)0.5546300509
Kurtosis15.87008762
Mean0.09067279457
Median Absolute Deviation (MAD)0.03205254968
Skewness1.608203407
Sum618.2977862
Variance0.002529069684
MonotonicityNot monotonic
2021-12-26T18:12:56.441218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
0.07061151781
 
< 0.1%
0.093764877691
 
< 0.1%
0.07547716961
 
< 0.1%
0.043013769781
 
< 0.1%
0.034272971091
 
< 0.1%
0.14309642071
 
< 0.1%
0.040365589091
 
< 0.1%
0.02876001871
 
< 0.1%
0.041144799911
 
< 0.1%
Other values (6809)6809
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.00078401106731
< 0.1%
0.00084674838031
< 0.1%
0.0010432669071
< 0.1%
0.0014813246761
< 0.1%
0.0015768544561
< 0.1%
0.0017983400621
< 0.1%
0.0019866775581
< 0.1%
0.0026768702631
< 0.1%
0.002819856451
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.34314273671
< 0.1%
0.33684412521
< 0.1%
0.32391738281
< 0.1%
0.30768635421
< 0.1%
0.3027649021
< 0.1%
0.29659144791
< 0.1%
0.28291279531
< 0.1%
0.27497152461
< 0.1%
0.27343315661
< 0.1%

Borrowing dependency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct4338
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3746542946
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size53.4 KiB
2021-12-26T18:12:56.743885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3696371815
Q10.3701678436
median0.3726243226
Q30.3762707372
95-th percentile0.3847602123
Maximum1
Range1
Interquartile range (IQR)0.006102893646

Descriptive statistics

Standard deviation0.01628616336
Coefficient of variation (CV)0.04346984297
Kurtosis802.6464927
Mean0.3746542946
Median Absolute Deviation (MAD)0.002807085038
Skewness20.83889089
Sum2554.767635
Variance0.0002652391168
MonotonicityNot monotonic
2021-12-26T18:12:57.030760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.36963718151153
 
16.9%
0.369639418215
 
0.2%
0.369638299811
 
0.2%
0.36965283857
 
0.1%
0.3696573126
 
0.1%
0.36971882185
 
0.1%
0.3706191025
 
0.1%
0.37072422795
 
0.1%
0.37157082695
 
0.1%
0.36984743335
 
0.1%
Other values (4328)5602
82.2%
ValueCountFrequency (%)
01
 
< 0.1%
0.18712409111
 
< 0.1%
0.26203523731
 
< 0.1%
0.27074279271
 
< 0.1%
0.27972546481
 
< 0.1%
0.29589360361
 
< 0.1%
0.3570556251
 
< 0.1%
0.35744034111
 
< 0.1%
0.36963718151153
16.9%
0.369638299811
 
0.2%
ValueCountFrequency (%)
11
< 0.1%
0.95481935121
< 0.1%
0.88743477161
< 0.1%
0.73461079931
< 0.1%
0.66902566191
< 0.1%
0.61365003811
< 0.1%
0.52944307881
< 0.1%
0.52835603231
< 0.1%
0.51423896681
< 0.1%
0.46536549141
< 0.1%

Current Liability to Current Assets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct6819
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03150636575
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size53.4 KiB
2021-12-26T18:12:57.497471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.007755176684
Q10.01803366571
median0.02759714285
Q30.03837461585
95-th percentile0.06425132425
Maximum1
Range1
Interquartile range (IQR)0.02034095015

Descriptive statistics

Standard deviation0.03084468845
Coefficient of variation (CV)0.9789986157
Kurtosis310.9992346
Mean0.03150636575
Median Absolute Deviation (MAD)0.01009095905
Skewness13.18866055
Sum214.841908
Variance0.0009513948058
MonotonicityNot monotonic
2021-12-26T18:12:57.796729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
0.03814820851
 
< 0.1%
0.032875537931
 
< 0.1%
0.029379354921
 
< 0.1%
0.02700977551
 
< 0.1%
0.039215183831
 
< 0.1%
0.021676723341
 
< 0.1%
0.037089954411
 
< 0.1%
0.033969491281
 
< 0.1%
0.015891581131
 
< 0.1%
Other values (6809)6809
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.0001224443031
< 0.1%
0.00021439695311
< 0.1%
0.0002200308861
< 0.1%
0.00027934027591
< 0.1%
0.00077035641771
< 0.1%
0.00082516499161
< 0.1%
0.00083381953051
< 0.1%
0.0013029465041
< 0.1%
0.0013229971691
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.91681425291
< 0.1%
0.65066098471
< 0.1%
0.61172381231
< 0.1%
0.46067477531
< 0.1%
0.42467691531
< 0.1%
0.40956299591
< 0.1%
0.40090487511
< 0.1%
0.35608428661
< 0.1%
0.353538781
< 0.1%

Liability to Equity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
UNIQUE

Distinct6819
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2803651538
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size53.4 KiB
2021-12-26T18:12:58.058528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2755735124
Q10.2769442426
median0.2787775836
Q30.2814491856
95-th percentile0.2888978503
Maximum1
Range1
Interquartile range (IQR)0.004504942962

Descriptive statistics

Standard deviation0.01446322358
Coefficient of variation (CV)0.05158709411
Kurtosis1209.203491
Mean0.2803651538
Median Absolute Deviation (MAD)0.002112616367
Skewness27.45946714
Sum1911.809984
Variance0.0002091848362
MonotonicityNot monotonic
2021-12-26T18:12:58.233656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
0.28026927811
 
< 0.1%
0.28365108781
 
< 0.1%
0.28452117671
 
< 0.1%
0.27533011251
 
< 0.1%
0.27581641181
 
< 0.1%
0.27571343171
 
< 0.1%
0.27607813441
 
< 0.1%
0.2837452581
 
< 0.1%
0.27552782431
 
< 0.1%
Other values (6809)6809
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.13350290481
< 0.1%
0.18279035891
< 0.1%
0.19916210321
< 0.1%
0.20922171391
< 0.1%
0.21878527461
< 0.1%
0.25927979841
< 0.1%
0.26522005791
< 0.1%
0.27477908321
< 0.1%
0.274785091
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.74535200441
< 0.1%
0.65234690161
< 0.1%
0.64369182611
< 0.1%
0.4843181411
< 0.1%
0.43590736071
< 0.1%
0.4028672991
< 0.1%
0.38787414711
< 0.1%
0.36835704591
< 0.1%
0.36310882451
< 0.1%

Current Liabilities/Equity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
UNIQUE

Distinct6819
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3314098006
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size53.4 KiB
2021-12-26T18:12:58.437201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3269614666
Q10.3280958417
median0.3296851331
Q30.3323224048
95-th percentile0.3406930272
Maximum1
Range1
Interquartile range (IQR)0.004226563123

Descriptive statistics

Standard deviation0.01348802791
Coefficient of variation (CV)0.04069894096
Kurtosis1171.460092
Mean0.3314098006
Median Absolute Deviation (MAD)0.001868918255
Skewness23.79916473
Sum2259.88343
Variance0.0001819268969
MonotonicityNot monotonic
2021-12-26T18:12:58.667557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
0.32778018591
 
< 0.1%
0.34575150281
 
< 0.1%
0.32923124651
 
< 0.1%
0.33668462231
 
< 0.1%
0.33763544041
 
< 0.1%
0.34912215111
 
< 0.1%
0.34867435431
 
< 0.1%
0.32747150021
 
< 0.1%
0.32805306911
 
< 0.1%
Other values (6809)6809
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.15381128981
< 0.1%
0.23439063461
< 0.1%
0.24105268351
< 0.1%
0.24632496811
< 0.1%
0.29490640391
< 0.1%
0.31491257291
< 0.1%
0.32039532321
< 0.1%
0.32617453911
< 0.1%
0.32620587841
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.77588978711
< 0.1%
0.62781687311
< 0.1%
0.53663485011
< 0.1%
0.52605196421
< 0.1%
0.52302210741
< 0.1%
0.43779425691
< 0.1%
0.41463949821
< 0.1%
0.40087708071
< 0.1%
0.39941907421
< 0.1%

Net Income to Total Assets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct6819
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.80776022
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size53.4 KiB
2021-12-26T18:12:58.845772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7476647524
Q10.7967498492
median0.8106190421
Q30.8264545295
95-th percentile0.858852674
Maximum1
Range1
Interquartile range (IQR)0.02970468035

Descriptive statistics

Standard deviation0.04033219153
Coefficient of variation (CV)0.04993089599
Kurtosis43.73442462
Mean0.80776022
Median Absolute Deviation (MAD)0.01446272251
Skewness-3.684096776
Sum5508.11694
Variance0.001626685674
MonotonicityNot monotonic
2021-12-26T18:12:59.019506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
0.84639658481
 
< 0.1%
0.81299487881
 
< 0.1%
0.78708156221
 
< 0.1%
0.59171622281
 
< 0.1%
0.79827467371
 
< 0.1%
0.84619131541
 
< 0.1%
0.81978461631
 
< 0.1%
0.82856059961
 
< 0.1%
0.92315505191
 
< 0.1%
Other values (6809)6809
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.22479204081
< 0.1%
0.41180922461
< 0.1%
0.41262098761
< 0.1%
0.42099546261
< 0.1%
0.42375528871
< 0.1%
0.43556895441
< 0.1%
0.45391591241
< 0.1%
0.46657744851
< 0.1%
0.48183625091
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.98287925261
< 0.1%
0.98131508411
< 0.1%
0.95932011681
< 0.1%
0.94432780761
< 0.1%
0.93835982621
< 0.1%
0.93440239211
< 0.1%
0.93231838131
< 0.1%
0.92519945411
< 0.1%
0.92481887421
< 0.1%

ROA(A) before interest and % after tax
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3151
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5586249159
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size53.4 KiB
2021-12-26T18:12:59.192891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4567433493
Q10.5355429568
median0.55980157
Q30.5891572176
95-th percentile0.6538922809
Maximum1
Range1
Interquartile range (IQR)0.05361426079

Descriptive statistics

Standard deviation0.06562003103
Coefficient of variation (CV)0.1174670681
Kurtosis9.038784836
Mean0.5586249159
Median Absolute Deviation (MAD)0.02643916267
Skewness-1.033726837
Sum3809.263301
Variance0.004305988473
MonotonicityNot monotonic
2021-12-26T18:12:59.369509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.559692542513
 
0.2%
0.568251199313
 
0.2%
0.55418665510
 
0.1%
0.56312690810
 
0.1%
0.557893589210
 
0.1%
0.566016136110
 
0.1%
0.558929350210
 
0.1%
0.56132795469
 
0.1%
0.58062581779
 
0.1%
0.54055822079
 
0.1%
Other values (3141)6716
98.5%
ValueCountFrequency (%)
01
< 0.1%
0.0069232446581
< 0.1%
0.05718491061
< 0.1%
0.069286960311
< 0.1%
0.090165721761
< 0.1%
0.090710859141
< 0.1%
0.091255996511
< 0.1%
0.10662887051
< 0.1%
0.11993022241
< 0.1%
0.12151112081
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.98473615351
< 0.1%
0.9545355431
< 0.1%
0.94706716091
< 0.1%
0.94270606191
< 0.1%
0.87559965111
< 0.1%
0.83171609251
< 0.1%
0.82015918011
< 0.1%
0.81601613611
< 0.1%
0.80925643261
< 0.1%

ROA(B) before interest and depreciation after tax
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3160
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5535887094
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size53.4 KiB
2021-12-26T18:12:59.571230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4601852348
Q10.5272766208
median0.5522779592
Q30.5841051448
95-th percentile0.6470046576
Maximum1
Range1
Interquartile range (IQR)0.05682852401

Descriptive statistics

Standard deviation0.06159480929
Coefficient of variation (CV)0.1112645693
Kurtosis7.929073722
Mean0.5535887094
Median Absolute Deviation (MAD)0.02773167728
Skewness-0.7635637192
Sum3774.921409
Variance0.003793920532
MonotonicityNot monotonic
2021-12-26T18:12:59.799656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.552492103410
 
0.1%
0.558220461510
 
0.1%
0.53878687310
 
0.1%
0.551474918410
 
0.1%
0.5435515829
 
0.1%
0.54344450999
 
0.1%
0.55404464919
 
0.1%
0.54050002689
 
0.1%
0.55072541369
 
0.1%
0.55227795929
 
0.1%
Other values (3150)6725
98.6%
ValueCountFrequency (%)
01
< 0.1%
0.033513571391
< 0.1%
0.054820921891
< 0.1%
0.088388029341
< 0.1%
0.09176080091
< 0.1%
0.11221157451
< 0.1%
0.11242571871
< 0.1%
0.17163659721
< 0.1%
0.18320038551
< 0.1%
0.20675625031
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.95776005141
< 0.1%
0.93259810481
< 0.1%
0.86915787781
< 0.1%
0.82338454951
< 0.1%
0.81455110021
< 0.1%
0.81026821561
< 0.1%
0.80389742491
< 0.1%
0.79822260291
< 0.1%
0.79645591311
< 0.1%

ROA(C) before interest and depreciation before interest
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3333
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5051796332
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size53.4 KiB
2021-12-26T18:13:00.019150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4130990104
Q10.4765270804
median0.5027056013
Q30.5355628138
95-th percentile0.6016233608
Maximum1
Range1
Interquartile range (IQR)0.05903573344

Descriptive statistics

Standard deviation0.06068563875
Coefficient of variation (CV)0.1201268514
Kurtosis6.390770202
Mean0.5051796332
Median Absolute Deviation (MAD)0.02905474577
Skewness-0.3239410032
Sum3444.819919
Variance0.003682746751
MonotonicityNot monotonic
2021-12-26T18:13:00.225529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.490128211412
 
0.2%
0.51645298111
 
0.2%
0.513820504110
 
0.1%
0.49919563210
 
0.1%
0.50192560829
 
0.1%
0.48037829678
 
0.1%
0.50606932198
 
0.1%
0.49812314148
 
0.1%
0.51464924688
 
0.1%
0.49505191838
 
0.1%
Other values (3323)6727
98.7%
ValueCountFrequency (%)
01
< 0.1%
0.024277287571
< 0.1%
0.066933164331
< 0.1%
0.081411787651
< 0.1%
0.082825525281
< 0.1%
0.10232535471
< 0.1%
0.12645639351
< 0.1%
0.16058109491
< 0.1%
0.1968020281
< 0.1%
0.20196948281
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.97153024911
< 0.1%
0.86496368161
< 0.1%
0.81801784231
< 0.1%
0.78798810511
< 0.1%
0.78013942381
< 0.1%
0.77526446641
< 0.1%
0.75981085171
< 0.1%
0.75513089261
< 0.1%
0.75464339691
< 0.1%

Net worth/Assets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4208
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.886822915
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size53.4 KiB
2021-12-26T18:13:00.657620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7973818666
Q10.8511956949
median0.8885932823
Q30.9271094718
95-th percentile0.9677812377
Maximum1
Range1
Interquartile range (IQR)0.07591377694

Descriptive statistics

Standard deviation0.05392030606
Coefficient of variation (CV)0.06080166079
Kurtosis10.72972726
Mean0.886822915
Median Absolute Deviation (MAD)0.03803246969
Skewness-0.9807961168
Sum6047.245458
Variance0.002907399406
MonotonicityNot monotonic
2021-12-26T18:13:00.855194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8845421296
 
0.1%
0.88708165796
 
0.1%
0.91084439346
 
0.1%
0.88052120816
 
0.1%
0.87178401916
 
0.1%
0.85984218646
 
0.1%
0.87659098476
 
0.1%
0.8932793186
 
0.1%
0.94990476775
 
0.1%
0.87553284765
 
0.1%
Other values (4198)6761
99.1%
ValueCountFrequency (%)
01
< 0.1%
0.4745895941
< 0.1%
0.66151706621
< 0.1%
0.66810774861
< 0.1%
0.67306587661
< 0.1%
0.67862865441
< 0.1%
0.68724491341
< 0.1%
0.69462164041
< 0.1%
0.70480998881
< 0.1%
0.70629138071
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99972790761
< 0.1%
0.99963721011
< 0.1%
0.99942558271
< 0.1%
0.99891163041
< 0.1%
0.99848837561
< 0.1%
0.99812558581
< 0.1%
0.99791395831
< 0.1%
0.99685582131
< 0.1%
0.99646279891
< 0.1%

Persistent EPS in the Last Four Seasons
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1358
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2288128526
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size53.4 KiB
2021-12-26T18:13:01.054647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.192304056
Q10.2147111657
median0.2245438215
Q30.2388200813
95-th percentile0.2766379881
Maximum1
Range1
Interquartile range (IQR)0.02410891557

Descriptive statistics

Standard deviation0.03326261308
Coefficient of variation (CV)0.1453703876
Kurtosis81.62179022
Mean0.2288128526
Median Absolute Deviation (MAD)0.0117235511
Skewness5.135963053
Sum1560.274842
Variance0.001106401429
MonotonicityNot monotonic
2021-12-26T18:13:01.235274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.214900255332
 
0.5%
0.218209322127
 
0.4%
0.218682045926
 
0.4%
0.218587501226
 
0.4%
0.21461662126
 
0.4%
0.218965680225
 
0.4%
0.218492956423
 
0.3%
0.225111090123
 
0.3%
0.220100217523
 
0.3%
0.219249314623
 
0.3%
Other values (1348)6565
96.3%
ValueCountFrequency (%)
01
< 0.1%
0.078566701331
< 0.1%
0.0795121491
< 0.1%
0.084239387351
< 0.1%
0.1018247142
< 0.1%
0.10702467621
< 0.1%
0.10721376571
< 0.1%
0.10957738491
< 0.1%
0.11231918311
< 0.1%
0.11392644421
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.77952160351
< 0.1%
0.7626926351
< 0.1%
0.72752198171
< 0.1%
0.66219154771
< 0.1%
0.53474520191
< 0.1%
0.52254892691
< 0.1%
0.51536352461
< 0.1%
0.48548737831
< 0.1%
0.47395291671
< 0.1%

target
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.4 KiB
0
6599 
1
 
220

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
06599
96.8%
1220
 
3.2%

Length

2021-12-26T18:13:01.391969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-26T18:13:01.496914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
06599
96.8%
1220
 
3.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-12-26T18:12:51.977920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:23.975929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:26.396477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:28.966749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:31.427515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:33.838423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:36.689362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:39.178282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:41.505326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:44.154191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:46.723577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:49.097052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:52.172755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:24.140132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:26.558343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:29.195946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:31.611486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:34.035264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:36.883750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:39.352803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:41.685291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:44.335065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:46.916720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:49.436151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:52.395669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:24.303474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:26.737730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:29.383016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:31.949891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:34.214201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:37.153788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:39.522340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:41.912618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:44.504260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:47.105409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:49.642004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:52.635316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:24.480010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:26.952357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:29.548704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:32.124202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:34.423815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:37.339144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:39.692640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:42.089835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:44.735975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:47.342841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:49.889881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:52.811566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:24.680052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:27.185372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:29.722246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:32.312921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:34.595520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:37.508675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:39.884144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:42.314977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:45.109022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:47.515429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:50.211758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:53.028674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:24.862195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:27.383617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:29.889470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:32.506526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:34.795832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:37.669719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:40.072976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:42.596073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:45.286920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:47.714106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:50.414589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:53.306342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:25.033468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:27.596080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:30.168917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:32.690433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:34.972002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:37.844131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:40.273787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:42.864205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:45.447338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:47.938749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:50.642317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:53.495790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:25.385698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:27.822273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:30.439487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:32.850177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:35.323812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:38.030181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:40.501218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:43.135463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:45.692778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:48.160588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:50.842792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:53.678869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:25.548370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:28.002903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:30.621588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:33.040981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:35.679747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:38.232143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:40.707827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:43.340138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:45.891074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:48.334621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:51.065163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:53.853724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:25.721049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:28.184317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:30.798706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:33.244788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-12-26T18:12:51.418509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:54.338112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:26.214335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:28.757134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:31.155844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:33.633001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:36.475904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:38.982856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:41.335691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:43.949118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:46.544558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:48.928003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-26T18:12:51.811831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-12-26T18:13:01.614405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-26T18:13:01.984332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-26T18:13:02.315922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-26T18:13:02.648126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-26T18:12:54.764191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-26T18:12:55.253392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Debt ratio %Current Liability to AssetsBorrowing dependencyCurrent Liability to Current AssetsLiability to EquityCurrent Liabilities/EquityNet Income to Total AssetsROA(A) before interest and % after taxROA(B) before interest and depreciation after taxROA(C) before interest and depreciation before interestNet worth/AssetsPersistent EPS in the Last Four Seasonstarget
00.2075760.1473080.3902840.1182500.2902020.3390770.7168450.4243890.4057500.3705940.7924240.1691411
10.1711760.0569630.3767600.0477750.2838460.3297400.7952970.5382140.5167300.4642910.8288240.2089441
20.2075160.0981620.3790930.0253460.2901890.3347770.7746700.4990190.4722950.4260710.7924840.1805811
30.1514650.0987150.3797430.0672500.2817210.3315090.7395550.4512650.4577330.3998440.8485350.1937221
40.1065090.1101950.3750250.0477250.2785140.3307260.7950160.5384320.5222980.4650220.8934910.2125371
50.1804270.1390020.3814480.0995220.2850870.3355340.7104200.4151770.4191340.3886800.8195730.1744351
60.2161020.1159200.3849990.0607650.2925040.3373920.7366190.4457040.4361580.3909230.7838980.1614820
70.1082020.0870420.3742190.0302010.2786070.3298040.8153500.5709220.5590770.5083610.8917980.2252060
80.0585910.0580600.3702530.0217100.2764230.3280930.8036470.5451370.5432840.4885190.9414090.2183980
90.1212930.0945630.3745090.0254940.2793880.3304090.8041950.5509160.5429630.4956860.8787070.2178310

Last rows

Debt ratio %Current Liability to AssetsBorrowing dependencyCurrent Liability to Current AssetsLiability to EquityCurrent Liabilities/EquityNet Income to Total AssetsROA(A) before interest and % after taxROA(B) before interest and depreciation after taxROA(C) before interest and depreciation before interestNet worth/AssetsPersistent EPS in the Last Four Seasonstarget
68090.1199630.0847630.3716410.0878640.2793030.3299430.7765350.5000550.5009370.4487400.8800370.2080930
68100.1421530.1494510.3696370.0279950.2809050.3337610.8469950.6480050.6317250.5789010.8578470.2715330
68110.0878860.0269840.3735690.0215570.2775880.3271940.8158440.5678700.5658230.5180620.9121140.2320130
68120.0624600.0647630.3696390.0227210.2765600.3283490.8067430.5494980.5378770.4916390.9375400.2186820
68130.1470810.0938290.3745870.0480550.2813250.3310980.7940280.5311270.5260990.4776000.8529190.2132930
68140.1246180.1038380.3738230.0279510.2796060.3309140.7999270.5394680.5432300.4936870.8753820.2166020
68150.0992530.0899010.3725050.0314700.2781320.3297530.7997480.5382690.5241720.4751620.9007470.2166970
68160.0389390.0244140.3696370.0075420.2757890.3269210.7977780.5337440.5206380.4727250.9610610.2109290
68170.0869790.0831990.3696490.0229160.2775470.3292940.8118080.5599110.5540450.5062640.9130210.2283260
68180.0141490.0185170.3700490.0055790.2751140.3266900.8159560.5701050.5495480.4930530.9858510.2277580